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Impact and cost-effectiveness of current and future tuberculosis diagnostics: the contribution of modelling
editorial
Author(s):
D. W. Dowdy
*
,
,
R. Houben
† ,
T. Cohen
‡ ,
M. Pai
§ ,
F. Cobelens
¶ ,
A. Vassall
# ,
N. A. Menzies
** ,
G. B. Gomez
¶ ,
I. Langley
†† ,
S. B. Squire
†† ,
R. White
† ,
for the TB MAC meeting participants
Publication date (Print):
September 2014
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
SUMMARY
The landscape of diagnostic testing for tuberculosis (TB) is changing rapidly, and stakeholders need urgent guidance on how to develop, deploy and optimize TB diagnostics in a way that maximizes impact and makes best use of available resources. When decisions must be made with only incomplete or preliminary data available, modelling is a useful tool for providing such guidance. Following a meeting of modelers and other key stakeholders organized by the TB Modelling and Analysis Consortium, we propose a conceptual framework for positioning models of TB diagnostics. We use that framework to describe modelling priorities in four key areas: Xpert ® MTB/RIF scale-up, target product profiles for novel assays, drug susceptibility testing to support new drug regimens, and the improvement of future TB diagnostic models. If we are to maximize the impact and cost-effectiveness of TB diagnostics, these modelling priorities should figure prominently as targets for future research.
RESUME
Le paysage des tests de laboratoire de la tuberculose (TB) change rapidement et les parties prenantes ont besoin de directives urgentes sur la manière d'élaborer, de diffuser et d'optimiser le diagnostic de la TB de façon à maximiser son impact et faire le meilleur usage des ressources disponibles. Quand il faut prendre des décisions basées sur les données incomplètes ou préliminaires qui sont les seules disponibles, la modélisation est un outil utile pour fournir ce type de directive. A la suite d'une réunion de modélisateurs et d'autres intervenants majeurs organisée par le « TB Modelling and Analysis Consortium', nous proposons un cadre conceptuel pour intégrer de nouveaux modèles de diagnostic de la TB. Nous utilisons ce cadre pour décrire les priorités de la modélisation dans quatre domaines principaux : l'expansion du test Xpert ® MTB/RIF, le ciblage de produits pour de nouveaux tests, les tests de pharmaco-sensibilité afin d'élaborer de nouveaux protocoles thérapeutiques et la nécessité d'améliorer les modèles futurs de diagnostic de la TB. Si nous voulons maximiser l'impact et la rentabilité du diagnostic de la TB, ces priorités doivent être les cibles principales de la recherche future.
RESUMEN
El panorama de las pruebas diagnósticas de la tuberculosis (TB) está evolucionando rápidamente y los interesados directos precisan con urgencia directrices en materia de desarrollo, despliegue y optimización de estos métodos, de manera que se obtenga el máximo impacto, mediante el uso óptimo de los recursos existentes. Cuando se deben adoptar decisiones solo con base en datos incompletos o preliminares, la modelización aparece como un instrumento útil que puede aportar esta orientación. Tras una reunión con modeladores y otros interesados clave, organizada por el ‘TB Modelling and Analysis Consortium’, se propuso un marco teórico destinado al posicionamiento de los modelos de pruebas diagnósticas de la TB. A partir de este marco, se describen las prioridades de la modelización en cuatro esferas principales: las estrategias de ampliación de escala de la prueba Xpert ® MTB/RIF, la definición de las características de las nuevas pruebas, las pruebas de sensibilidad a los medicamentos que fundamenten los nuevos regímenes terapéuticos y las deficiencias que se deben superar a fin de mejorar los futuros modelos de métodos diagnósticos de la TB. Si se busca obtener el máximo impacto y la mayor rentabilidad de los medios diagnósticos, estas prioridades de modelización deben ocupar un puesto prominente en los objetivos de las investigaciones futuras.
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Most cited references30
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Financial burden for tuberculosis patients in low- and middle-income countries: a systematic review
Tadayuki Tanimura, Ernesto Jaramillo, Diana Weil … (2014)
Introduction An estimated 100 million people fall below the poverty line each year because of the financial burden of disease [1]. Tuberculosis (TB), which mostly affects the poorest of the poor, is an example of a disease that can substantially contribute to the disease poverty trap [2, 3]. Most countries aim to provide TB diagnosis and treatment free of charge within public health services. Access to free TB care has expanded substantially over the past two decades through national efforts and global financial support [4]. However, many TB patients and families are still facing very high direct and indirect costs due to TB illness and care-seeking, hampering access and putting people at risk of financial ruin or further impoverishment [5, 6]. The World Health Organization (WHO) is developing a post-2015 Global TB Strategy, which highlights the need for all countries to progress towards universal health coverage to ensure “universal access to needed health services without financial hardship in paying for them,” [7] as well as social protection mechanisms for “income replacement and social support in the event of illness” [8, 9]. One of the tentative global targets for the strategy is “no TB-affected family facing catastrophic costs due to TB”, to be reached globally by 2020 [10]. This target reflects the anticipated combined financial risk protection effect of the progressive realisation of both universal health coverage and social protection. Universal health coverage has long been on the global TB control agenda, which stresses the need for universally accessible, affordable and patient-centred services [2, 11–13]. Social protection has emerged more recently as a key policy area for TB care and prevention [10, 14–17]. Social protection involves schemes to cover costs beyond direct medical costs, including compensation of lost income. Examples of social protection schemes include sickness insurance, disability grants, other conditional or unconditional cash transfers, food assistance, travel vouchers and other support packages [14]. Such schemes exist in most countries, but may not be fully implemented due to inadequate financing or insufficient capacities of the healthcare and social welfare systems [18]. Furthermore, they may not include TB patients among those eligible [10, 14, 17]. In order to inform the development of appropriate strategies for improved access and financial risk protection for people with TB, we have undertaken a systematic literature review on medical costs, non-medical costs, as well as income loss for TB patients and affected households in different settings, as well as the main drivers of those costs. Methods Eligibility criteria This review includes studies written in English, conducted in low- and middle-income countries and published from inception to March 31, 2013, reporting data on medical costs, non-medical costs and/or income loss incurred by TB patients during the process of seeking and receiving care for TB, as well as coping strategies. We excluded studies in which only total cost was reported without any disaggregation into direct and indirect costs and studies using secondary data derived from other published articles. Information sources and search strategies We searched the following electronic databases: PubMed; Global Information Full Text; Index Medicus for Africa, South-East Asia, Eastern Mediterranean region, and Western Pacific region; and Literatura Latinoamericana y del Caribe en Ciencias de la Salud. Furthermore, we checked reference lists of reviewed studies [19–22] and of documents and meeting reports from the World Bank and WHO websites. The search terms were “tuberculosis” (tuberculosis, TB, or tuberculosis as a MeSH Term in PubMed) and “cost” (cost(s), expense(s), economic, expenditure(s), payment(s), out-of-pocket, financial, impoverishment, or catastrophic). Data extraction We extracted the following background information: country, location, urban/rural, year of the publication and data collection, setting characteristics, and method of data collection and calculation of costs and income loss. We stratified, to the extent data allowed, into the following cost components: direct medical costs (consultations, tests, medicines and hospitalisation, etc.), direct non-medical cost (transport and food during healthcare visits, etc.) and indirect costs (lost income). If possible, cost was stratified by socioeconomic status, hospitalisation/ambulatory treatment, drug-resistant TB or drug-susceptible TB, and sex. The cost components were extracted separately for the pre- and post-TB diagnosis period, if available. Pre-TB treatment costs are those incurred between the onset of symptoms and the initiation of treatment for TB. In all studies, this data was collected retrospectively at a point in time after diagnosis. Post-diagnostic costs are those incurred from TB diagnosis to completion of treatment. Costs during treatment were either collected prospectively through repeat surveys of patients in treatment or retrospectively. If retrospectively collected at some point during treatment, the cost was then extrapolated to the planned treatment duration in most studies. We also extracted data on costs as a percentage of reported individual and/or household income, if available. For all studies done in countries for which both “gross average nominal monthly wage” in the International Labour Organization's global wage database [23] and “income share held by lowest 20%” in the World Bank's online data [24] were available, we also computed total costs as percentage of average annual income and percentage of annual income in the lowest quintile for each respective country. The latter was done under the assumption that TB mostly affects the poorest quintile in any given setting. We used the available data for the nearest year to a year of the data collection. Where available, we extracted information about mechanisms for coping with financial burden, such as taking a loan or selling property. Summary measures and synthesis of results The focus of the analysis was on the distribution of the magnitude and components of costs across settings. We also report descriptive analyses of the central tendencies of the data. For each variable we provide the range of reported means across studies, unweighted average of means (with standard deviation), and the median and interquartile range of means. When a mean value for all study subjects in a given study was not available, we re-calculated an unweighted mean across subgroup within the study. We also report the range and unweighted average of percentage distributions of different cost components. Under the assumption of large heterogeneity, we decided a priori to focus the analysis on the variations across studies, while providing summary estimates for some variables as an indication of central tendencies across studies. We opted not to calculate confidence intervals for the unweighted average of means, in order to avoid a false impression of precision for the measures of central tendency. If one study reported data from several different country surveys, each survey was analysed as a separate observation. Data availability for variables of interest varied across studies. Summary statistics are therefore based on different number of studies. Mean cost values were available from 44 studies (reporting 47 surveys) of the 49 studies (reporting 52 surveys). Only median values were reported in five studies. We therefore did not use median values for summarising the key variables across studies. However, where applicable, median values were used for comparison of different subgroups within studies. Costs in international dollars ($) were calculated by multiplying raw cost data in US dollars, the exchange rate with the local currency for the year of data collection and the cumulative inflation rate [25] from the year of data collection to 2010 (latest year of data availability), and divided it by the purchasing power parities conversion factor [26]. The exchange rates reported in reviewed articles were preferentially used for the calculation and, in the absence of them, we used the exchange rates from the “National Accounts Main Aggregates Database” of the United Nations Statistics Division [27] and the exchange rate of Sudan from UN data [28] as the data of Sudan in a studied year is missing in the former source. Results 49 studies fulfilled the inclusion criteria (fig. 1). One study without cost data was included since it provided data on coping strategies [29]. Details about included studies are provided in table 1. Figure 1– Flow chart of literature search. Table 1– Type of costs Study Mean/ median/both Phase coverage# Components of Breakdown of Disaggregation by Costs as percentage of annual income Coping mechanism Direct costs Direct med. costs Direct non-med. costs Hosp. cost Lost income Direct med. costs Direct non-med. costs Lost income Before/ during treatment¶ Hosp./amb. MDR/ non-MDR SES Sex Individ. House. LQ Muniyandi (India, 2000) [30] Both Both √ √ D&I √ √ √ √ √ Rajeswari (India, 1995+) [31] Both Both √ √ √ √ √ √ Mauch (Ghana, 2009+) [5] Both Both √ √ √ √ √ √ √ All √ √ √ √ Mauch (Vietnam, 2009+) [5] Both Both √ √ √ √ √ D&I √ √ √ √ Mauch (Dominican Republic, 2009+) [5] Both Both √ √ √ √ √ D&I √ √ √ √ Karki (Nepal, 2002) [32] Both Both √ √ √ √ √ √ √ √ √ √ Xu (China, 2002) [33] Both Both √ √ √ D √§ √§ √§ Kemp (Malawi, 2001) [34] Both Before √ √ √ √ √ √ √ √ Needham (Zambia, 1995) [35] Both Before √ √ √ √ √ √ √ √ √ Mesfin (Ethiopia, 2005) [36] Both Before √ √ √ √ √ √ √ √ √ √ Jacquet (Haiti, 2003) [37] Mean Both √ √ D&I √ Lönnroth (Myanmar, 2004) [38] Mean Both √ √ √ √ √ √ All √ √ √ Gibson (Sierra Leone, 1994) [39] Mean Both √ D Kamolratanakul (Thailand, 1996/97) [40] Mean Both √ √§ √§ √ √ √ D √ √ √ √ Wyss (Tanzania, 1996) [41] Mean Both √ √ √ √ √ √ Saunderson (Uganda, 1992) [42] Mean Both √ √ √ √ √ Sinanovic (South Africa, 1998) [43] Mean Both √ √ √ √ √ Jackson (China, 2002–2005) [44] Mean Both √ √ √ √ √ √ √ √ √ Pantoja (India, 2005) [45] Mean Both √ √ √ √ √ √ √ √ All √ √ √ √ Ananthakrishnan (India, 2007) [46] Mean Both √ √ √ √ All √ √ √ √ Othman (Yemen, 2008/09) [47] Mean Both √ √ √ √ √ √ Pichenda (Cambodia, 2008) [48] Mean Both √ √ √ √ √ √ All √ √ √ Ayé (Tajikistan, 2006/07) [49] Mean Both √ √ √ √ √ √ D&I √ √ √ √ Steffen (Brazil, 2007/08) [50] Mean Both √ √ √ √ √ √ √ √ All √ √ √ √ Rouzier (Ecuador, 2007) [51] Mean Both √ √§ √§ √ √ √ √ √ √ √ John (India, 2007) [52] Mean Both √ √ √ √ √ √ √ √ All √ √ √ √ √ Muniyandi (India, 2000) [53] Mean Both √ √ √ Elamin (Malaysia, 2002) [54] Mean Both √ √ √ √ √ √ Mahendradhata (Indonesia, 2004/05) [55] Mean Both √ √ √ √ √ √ Sinanovic (South Africa, 2002) [56] Mean Both √ √ √ √ √ Vassall (Ethiopia, 2005) [57] Mean Both √ √ √ √ √ All √ √ Costa (Brazil, 2000) [58] Mean Both √ √ √ √ √ √ √ √ √ √ El Sony (Sudan, 1998/99) [59] Mean Both √ √ √ Khan (Pakistan, 1997/98) [60] Mean Both √ √ √ Umar (Nigeria, 2008) [61] Mean Both √ √ √ √§ Vassall (Syria, 1999) [62] Mean Both √ √ √ Vassall (Egypt, 1999) [62] Mean Both √ √ √ Meng (China, 2000) [63] Mean Both √ √ √§ Zhan (China, 2000/01) [64] Mean Bothƒ √ √ √ Dƒ √§ √§ Ray (India, 2003) [65] Mean Before √ √ √ √ Datiko (Ethiopia, 2007) [66] Mean Before √ √ √ √ √ √ Croft (Bangladesh, 1996) [67] Mean Before## √ √ √ √ √ √ √ Okello (Uganda, 1998) [68] Mean During √ √ √ √ √ √ √ Wandwalo (Tanzania, 2002) [69] Mean During √ √ √ √ Prado (Brazil, 2005/06) [70] Mean During √ √ √ √ √ √ Mirzoev (Nepal, 2001/02) [71] Mean During √ √ √ √ Jacobs (Russia, 1997) [72] Mean During √ √ √ √ Total number of surveys 47 (44 studies) 44 31 29 9 42 18 16 14 17 6 3 11 9 25 13 36 10 Mauch (Kenya, 2008) [73] Median Both √ √ √ √ Laokri (Burkina Faso, 2007/08) [74] Median Both √ Umar (Nigeria, 2008) [75] Median Both √ √ √ √ √ √ √ Aspler (Zambia, 2006) [76] Median Both √ √ √ √ √ √ D √ √ √ Liu (China, 2004) [77] Median Both √ √ √ D Total number of surveys 5 (5 studies) 5 3 3 0 2 2 2 0 2 3 0 1 2 0 0 0 1 The years in which the majority of data collection took place are provided for each study. Hosp.: hospitalisation; amb.: ambulatory; SES: socioeconomic status; individ.: individual annual income; house.: household annual income; LQ: lowest quintile. #: before treatment, during treatment, or before and during treatment (both). ¶: only direct costs (D); direct and indirect costs without medical and non-medical subcomponents (D&I); or all costs including medical and non-medical subcomponents (all). +: estimated year of data collection using the average gap of 4 years calculated from other articles. §: data are only for part of the costs and were excluded from the calculation of the average and figure 3. ƒ: costs of diagnosis are included in post-diagnosis. ##: data is before reaching facilities of national tuberculosis programme. Mean total costs ranged from $55 to $8198 across 40 surveys for which mean costs and conversion values were available, with an unweighted average of $847, and a median of $379. The proportion of direct medical costs out of total cost ranged from 0–62% (unweighted average 20%) across the 25 surveys that provided disaggregated data on direct medical, direct non-medical, and indirect costs. Direct non-medical costs ranged from 0–84% (unweighted average 20%) and indirect costs (income loss) from 16–94% (unweighted average 60%) of total cost (table 2). Table 2– Patient costs and distribution of costs from 25 surveys with disaggregated medical direct costs, non-medical direct costs and income loss Cost category Direct costs Indirect costs Total costs Medical costs Non-medical costs Unweighted average of mean costs $ (sd) (range) 296.8 (376.0) 450.8 (553.4) 738.1 (821.3) (21.9–1316.4) (29.8–2184.0) (54.6–3500.4) 144.9 (206.8) 152.0 (275.9) (0–801.7) (0–1271.4) Median (IQR) of mean costs $ 136.2 (58.0–304.9) 206.9 (109.0–486.3) 397.1 (155.4–1097.2) 50.0 (14.2–140.0) 32.1 (22.8–120.7) Unweighted average contribution % (range) 39.8 (6.2–83.7) 60.2 (16.3–93.8) 100 20.1 (0–62.4) 19.8 (0–83.7) IQR: interquartile range. Costs are quoted in international dollars. Eight studies fully disaggregated direct and indirect costs both before and during treatment. On average, costs incurred before TB treatment was initiated represented 50% of the total cost (fig. 2). While indirect costs dominated both before and during treatment, direct costs were relatively more important before than during treatment. Direct costs were driven mostly by medical costs before treatment and by non-medical costs during treatment. Figure 2– Breakdown of direct and indirect costs before and during treatment (eight studies). Percentages are proportion of respective sub-component cost out of the total cost. Across 18 studies that further disaggregated direct medical costs, the proportion of drug costs out of direct medical costs ranged from 0% to 86% (unweighted average of 34%), while the contribution from diagnostic and follow-up test costs ranged from 0% to 94% (unweighted average of 27%,) and hospitalisation costs from 0% to 71% (unweighted average of 24%). Transport costs (range 11–96%, unweighted average 50%), and food costs (range 0–89%, unweighted average 37%,) were the largest contributors to direct non-medical costs in 16 studies that disaggregated the direct non-medical costs. There was a large variation across studies in the mean total cost as percentage of income, with skewed distributions due to a few studies reporting very high costs (table 3 and fig. 3). Total cost as percentage of reported annual individual income ranged from 5% to 306% (unweighted average 58%, median 44%), while the total cost as percentage of reported household income ranged from 4% to 148% (unweighted average 39%, median 23%). Total cost as percentage of the average annual income in the lowest income quintile of the country of study ranged from 3% to 578% (unweighted average 89%, median 21%). Table 3– Costs as percentage of annual income Surveys n Direct costs % Lost income % Total costs % Range of total costs % Individual Reported income 22 Average of mean (SD) 21 (27) 37 (43) 58 (64) 5–306 Median of mean (IQR) 10 (5–23) 24 (12–37) 24 (12–37) Annual wage 35 Average of mean (SD) 9 (14) 21 (29) 30 (42) 0–211 Median of mean (IQR) 3 (2–12) 3 (2–12) 7 (4–41) Wage of lowest 20% 34 Average of mean (SD) 25 (42) 25 (42) 89 (139) 3–578 Median of mean (IQR) 8 (4–29) 14 (6–88) 21 (10–101) Reported household income 7 Average of mean (SD) 16 (17) 22 (29) 39 (46) 4–148 Median of mean (IQR) 11 (9–15) 14 (4–20) 23 (14–36) IQR: interquartile range. Figure 3– Costs as percentage of a) reported annual individual income, b) reported annual household income and c) annual wage of the lowest quintile. The far right bars are truncated and percentages are shown above. avg.: average across subgroups for which separate means were reported in the original study. MDR: multidrug resistant; TB: tuberculosis. In 12 studies that disaggregated data by socioeconomic status group, there was no consistent tendency of difference in the absolute total cost incurred. However, the five studies that reported the cost as percentage of the reported income specific to each group found that the cost was considerably higher among the lower socioeconomic status groups [30, 34, 38, 40, 46]. Among the three studies that disaggregated the total cost for patients with multidrug-resistant (MDR)-TB versus drug-susceptible TB, the cost was considerably higher for MDR-TB patients (fig. 3). The difference in indirect costs was larger than that of the direct costs in two studies [48, 51]. The total costs as percentage of reported individual income for MDR-TB patients and drug-susceptible TB patients in two of the three studies were 223% ($14 388) versus 31% ($2008) in Ecuador [51] and 76% ($2953) versus 24% ($923) in Cambodia [48]. For the third study, from Brazil, that calculated income loss based on reported income after TB diagnosis, the cost burden was similar for MDR-TB and drug-susceptible patients (34% versus 27% of reported annual income) [58]. In 11 studies that disaggregated the total costs between males and females there was no consistent tendency of difference in absolute total costs. However, in two studies in Nigeria and Zambia that also reported individual income by sex, the costs for females as percentage of reported income were significantly larger [75, 76]. Commonly reported coping mechanisms included taking a loan, selling household items, using savings, and transfers from relatives (table 4). The amounts were not reported. Table 4– Percentage of patients pursuing specific coping strategies Country, area, year of data collection Taking loan % Selling household items % Using own savings % Transfers from relatives % Ghana, urban and rural, 2009 [5] 47 37 Vietnam, urban and rural, 2009 [5] 17 5 Dominican Republic, urban and rural, 2009 [5] 45 19 Tajikistan, urban and rural, 2006/2007 [29] 30 49 30 India, rural, 2000 [30] 71 India, urban and rural, 1995 [31] Governmental hospitals 76 NGO-run hospitals 58 Private health facilities 68 Myanmar, urban, 2004 [38] Higher socioeconomic status 27 Lower socioeconomic status 55 Thailand, nationwide, 1996/97 [40] Income below poverty line 12 16 22 23 Income below average 9 7 21 21 Income above average 8 8 14 17 China, rural, 2002-05 [44] 8 45 66 Bangladesh, 1996 [67] 14 38 Kenya, 2008 [73] 57 NGO: nongovernment organisation. Discussion This review demonstrates that the economic burden of seeking TB care is often very high for patients and affected households. Clearly, accessing TB care and continuing treatment comes with a high risk of financial ruin or further impoverishment for many people. In most settings, income loss is a dominating reason for the high costs. However, the financial burden varies considerably both between individuals in the same setting and between settings. This should be expected as the burden is determined by a range of factors, such as socioeconomic status, clinical needs, health system structure, TB service delivery model, distance to health services, insurance coverage, capacity to work, existence of any social protection scheme, and effectiveness of informal social networks supporting patients and families. This review shows that, while costs are catastrophic for many patients, they are minimal for others. It is crucial to identify the factors that contribute to costs incurred and to financial ruin. Unfortunately, few studies provided sufficient details about the models and context of care to allow us to quantify the relative importance of the different factors. However, the available data hint at some key explanations and intervention entry points. Cost of medicines and diagnostic tests were important drivers of direct medical costs, despite TB medicines and basic TB-specific tests being free of charge in services linked to the national TB programme in most countries. Detailed accounts of which medicines and tests were accessed were not available from any of the studies, but authors of some studies speculated about several possible reasons for cost incurred: patients may not have been offered free medicines for drug-resistant TB; some patients pay for services outside national TB programme facilities, e.g. in the private sector; and costs of adjuvant medicines may have contributed. Hospitalisation was another key driver of direct costs. In some settings, patients are routinely hospitalised, especially if MDR-TB is diagnosed. The necessity of some medical procedures and routine hospitalisation is not substantiated. Ensuring use of evidence-based cost-effective diagnostic and treatment routines can reduce direct medical costs [49, 52]. The costs of appropriate services, within national programmes as well as outside, should be fully subsidised given the public health implications of failure to ensure access and use of quality TB care, the known low socioeconomic status of most TB patients, and recommended prioritisation of coverage of priority health interventions like for TB under universal health coverage objectives [78]. Ensuring provision of free-of-charge TB diagnosis and treatment also in private facilities have been shown to reduce the direct costs for patients [45, 79]. Transport and food costs accounted for a major part of direct non-medical costs for patients. Provision of transport vouchers, reimbursement schemes and food assistance could be used to reduce or compensate for such costs. Furthermore, decentralisation of patient supervision (including directly observed therapy), e.g. through community-based [43, 66] or workplace-based treatment [43], can reduce transport costs as well as income loss for patients. Minimising costs during treatment does not guarantee financial risk protection since a large part of the cost is often incurred before treatment starts. In addition, costs during the first 2 months of treatment tend to dominate the costs incurred during treatment [29, 57, 74]. Peaking costs around the time of diagnosis and treatment initiation may constitute one of the most powerful barriers for people ill with TB to complete the diagnostic search, to start treatment once diagnosed, and to adhering to treatment to cure. Therefore, effective intervention at the time of diagnosis and treatment initiation may have significant impact. Affordable health services, as well as social protection schemes, are needed to enable access, reduce delays and to compensate for direct and indirect costs. Social protection schemes cover general categories of vulnerable persons, such as those with disabilities or sickness or other causes of limited or reduced income. TB patients may in some settings meet criteria for such support. In other settings, TB-specific targeting may be in place for provision of specific packages of social support such as food stuffs or cash transfers, with or without means testing. This review identified two groups of TB patients that require special attention: people with MDR-TB and people in the lowest income brackets. For the first group, the debilitating nature of the disease, its long-term care, and associated income loss may put them at special risk for catastrophic costs. For the second group, low-income means that the relative costs of direct medical care and non-medical costs, as well as income loss due to precarious informal employment in many cases, may exacerbate already serious economic vulnerability and catastrophic costs may carry relatively greater impact. This study has several limitations. First, there may be both publication and selection bias that could limit the representativeness of the findings. All studies included only people who have been diagnosed with TB. Costs for those ill with TB who seek care but never get diagnosed may be very different, and could for example be dominated by progressing income loss due to untreated illness. Furthermore, most of the studies only included persons diagnosed and started on treatment within national TB programmes. Many people are treated in the private sector. Direct costs are often higher in the private sector than in facilities linked to the national programme [31, 55]. There is thus a bias towards surveys of public sector patients. Furthermore, there is inclusion bias with regards to some publication languages. Finally, the search strategy was not optimal for the inclusion of studies that only reported on copying mechanism. Secondly, there were large variations in how data were collected analysed and reported. In particular, the methods for calculating the income loss varied considerably. To accurately measure income loss is more difficult than to measure direct costs [80]. We could not find any clear patterns of methods used which affected cost estimations, except that the indirect costs in studies using reported income after diagnosis was lower than in other studies [58, 73]. Additional research is needed to validate different measurement approaches. Thirdly, the studies provided limited information about the health system context. This review provides a cross-sectional snapshot of the financial burden of TB across very different settings. The relevant drivers of costs and interventions to minimise costs will have to be determined locally, based on further local operational research. There is a “TB patient-cost toolkit” available to guide the design of local surveys [6]. Fourthly, while studies reported mean values (and median to a lesser extent), no study reported the full distribution of costs, the costs as a percentage of income, or the percentage of patients that had faced “catastrophic costs”. However, several possible definitions of “catastrophic costs” were discussed in the reviewed papers, including “>10% of monthly household income” [52], “>10% of annual household income” [61, 74]; “>40% of non-subsistence household income” [5, 44]; or “using non-reversible coping strategies” [29]. The WHO has proposed that “catastrophic health expenditure” be defined as direct healthcare expenditures corresponding to >40% of annual discretionary income (income after basic needs, such as food and housing) [7]. The World Bank has proposed a similar definition but has not specified a cut-off value [81]. Indirect costs of care and income loss are not included in these measures. The WHO's Global TB Programme is considering development of TB-specific indicators and target for reduction in catastrophic costs due to TB for patients and their families [10]. Here, all care-related expenditures, as well as income loss, are being considered as relevant elements of overall catastrophic costs. A threshold for TB-related “catastrophic costs” needs to be defined. One possible option would be to adopt the definition of “total costs corresponding to >10% of annual household income”, which has been proposed by Ranson [82] as appropriate for measuring catastrophic total costs. Incidence of impoverishment may also be considered. Another option is to use generic or locally defined irreversible coping strategies as proxy indicators for catastrophic costs. Further work is needed to assess the correlation between high total cost in relation to income and seemingly irreversible coping strategies.
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Point-of-Care Testing for Infectious Diseases: Diversity, Complexity, and Barriers in Low- And Middle-Income Countries
Nitika Pant Pai, Nitika Pant Pai, Caroline Vadnais … (2012)
Madhukar Pai and colleagues discuss a framework for envisioning how point-of-care testing can be applied to infectious diseases in low- and middle-income countries.
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- Abstract: found
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Rapid Diagnosis of Tuberculosis with the Xpert MTB/RIF Assay in High Burden Countries: A Cost-Effectiveness Analysis
Anna Vassall, Sanne C van Kampen, Hojoon Sohn … (2011)
Introduction Tuberculosis (TB) control in developing countries is hampered by the inadequate care that can be delivered on the basis of diagnosis by microscopy alone—an issue that is acute in populations with a high prevalence of HIV and/or multidrug resistant (MDR)-TB. It is estimated that 1.7 million people died from TB in 2009, many of them remaining undiagnosed [1]. The Xpert MTB/RIF assay (referred to as Xpert in this article), is a real-time PCR assay that is a design-locked, all-within-cartridge test, and that has demonstrated high performance and could be deployed in a range of low- and middle-income settings [2],[3]. It has recently been endorsed by the World Health Organization (WHO) as a promising new rapid diagnostic technology that has the potential for large-scale roll-out (www.who.int/tb/laboratory). Xpert is commercially produced and sold at concessional prices. However, because the price is considerably higher than that of smear microscopy, there is a concern among TB program managers and policy makers that Xpert may not be cost-effective in low- and middle-income settings. There is little previous research into the cost-effectiveness of TB diagnostics. A study considering a hypothetical TB diagnostic found that cost-effectiveness would be maximized by high-specificity, low-cost tests deployed in regions with poor infrastructure [4]. Other studies have examined the cost-effectiveness of culture, PCR, and novel methods for drug susceptibility testing such as line-probe assays (LPA). These studies all found that these diagnostic tests are potentially cost-effective [5]–[7]. However, because of their technical requirements, mycobacterial culture, PCR, and LPA can only be deployed in specialised laboratories. We present the first (to our knowledge) economic evaluation of the Xpert rapid test for TB. [2]. Methods The aim of this study was to assess whether Xpert is likely to result in an improvement of the cost-effectiveness of TB care in low- and middle-income settings. We did this by estimating the impact of Xpert on the costs and cost-effectiveness of TB care in three countries, using decision analytic modelling, comparing the introduction of Xpert to a base case of sputum microscopy and clinical diagnosis. The model's primary outcome measure is the cost per disability adjusted life year (DALY) averted. Our model followed a cohort of 10,000 individuals suspected of having TB through the diagnostic and treatment pathway, estimating costs and health gains. In the diagnostic pathway, the TB cases among the individuals with suspected TB were either diagnosed as having TB or not, depending on the test sensitivities in the pathway. Similarly, individuals with suspected TB who were not TB cases may have been diagnosed as having TB, depending on the pathway's test specificities. A diagnosis of TB was followed by treatment. Individuals with suspected TB completed the pathway when they were either cured, failed treatment, died, or, if they had no TB from the start, remained without TB. Three different diagnostic scenarios are compared (Figure 1). The base case is defined as two sputum microscopy examinations followed, in smear-negative individuals with suspected TB, by clinical diagnosis that might include chest X-ray and antibiotic trial [8]. The inclusion of an antibiotic trial (empirical treatment with one or more broad-spectrum antibiotics to exclude other infectious causes of pulmonary disease) is no longer part of the WHO diagnostic strategy for HIV-infected patients. However, in the clinics participating in the demonstration study from which the diagnostic performance parameters were sourced [2], an antibiotic trial was still commonly provided during the diagnostic process as an adjunct to the treatment of smear-negative individuals with suspected TB. Antibiotic trial was therefore included in the base case; the model assumed that for each country the use of antibiotic trial and chest X-ray was proportional to the observed use in the demonstration study clinics. In comparison, two alternative pathways involving Xpert were considered: (1) two smear examinations, if negative followed by Xpert on a single sputum specimen (“in addition to”); (2) Xpert instead of smear examination: single sputum specimen tested by Xpert for all individuals with suspected TB (“replacement of”). 10.1371/journal.pmed.1001120.g001 Figure 1 Simplified schematic of model. Each scenario included drug resistance testing of previously treated patients [9], either by conventional drug susceptibility testing (DST) or Xpert. All patients diagnosed with TB were treated using the standard WHO-recommended regimens. Patients awaiting DST results were started on first-line treatment (isoniazid [H], rifampicin [R], pyrazimamide [Z], and ethambutol [E] for 2 mo followed by HR for 4 mo for new patients, and HRZE for 3 mo with streptomycin added during the first 2 mo followed by HRE for 5 mo for patients with a history of previous TB treatment) and switched to second-line treatment when a DST result of rifampicin resistance became available. The second-line treatment regimens differed between the countries but commonly included a fluoroquinolone and an aminoglycoside (kanamycin, amikacin) or capreomycin in addition to one or more first-line drugs and ethionamode, cycloserine, and/or 4-aminosalicylic acid (PAS). If Xpert identified rifampicin resistance, this was confirmed by conventional DST or LPA as practice in the respective countries. LPA, used as a screening test on smear-positive sputum samples in South Africa, detects rifampicin resistance within 24 h by molecular methods. While awaiting this result, the patient was started on second-line treatment, but then switched to first-line treatment if resistance to rifampicin was not confirmed. TB cases that remained undiagnosed were assumed to return to the clinic after 3 mo, with 10% of undiagnosed cases becoming smear-positive within that time. Key model input parameters are shown in Table 1 and further details can be found in Text S1. The model was parameterised for three settings: India (low HIV prevalence, low MDR prevalence), Uganda (high HIV prevalence, low MDR prevalence), and South Africa (high HIV prevalence, high MDR prevalence). In each cohort, TB cases were characterized as: (1) new or previously treated, (2) HIV-negative or HIV-positive, and (3) MDR or drug susceptible. These proportions were sourced from country reports [1],[10],[11]. 10.1371/journal.pmed.1001120.t001 Table 1 Model inputs: cohort composition and diagnostic parameters, by country. Cohort Proportions and Diagnostic Parameters India South Africa Uganda Distribution Source Cohort proportions Smear-positive TB 0.1 0.1 0.1 Beta Model assumption Smear-positive TB among pulmonary TB cases, HIV-negative 0.723 0.723 0.723 Beta Demonstration study, all sites [2] Smear-positive TB among pulmonary TB cases, HIV-positive 0.446 0.446 0.446 Beta Demonstration study, all sites [2] Previous TB treatment among pulmonary TB cases 0.192 0.168 0.073 Beta WHO [1] Multidrug resistance, among new TB cases 0.023 0.066 0.011 Beta WHO [10] Multidrug resistance, among previously treated TB cases 0.172 0.245 0.117 Beta WHO [10], survey [11] HIV infection, among pulmonary TB cases 0.006 0.588 0.593 Beta WHO [1] Diagnostic parameters Sensitivity for diagnosing pulmonary TB (SEM) Xpert MTB RIF, smear-positive TB cases 0.983 (0.005) 0.983 (0.005) 0.983 (0.005) Beta Demonstration study, all sites [2] Xpert MTB RIF, smear-negative TB cases, HIV-negative 0.793 (0.025) 0.793 (0.025) 0.793 (0.025) Beta Demonstration study, all sites [2] Xpert MTB RIF, smear-negative cases, HIV-positive 0.718 (0.040) 0.718 (0.040) 0.718 (0.040) Beta Demonstration study, all sites [2] Smear microscopy (two slides), HIV-positive 0.723 (0.015) 0.723 (0.015) 0.723 (0.015) Beta Demonstration study, all sites [2] Smear microscopy (two slides), HIV-negative 0.446 (0.036) 0.446 (0.036) 0.446 (0.036) Beta Demonstration study, all sites [2] Mycobacterial culture 1 (—) 1 (—) 1 (—) Model assumption Clinical diagnosis 0.160 (0.073) 0.209 (0.039) 0.444 (0.096) Beta Demonstration study [2] Proportion culture-positive individuals with suspected TB who had chest X-ray 0.032 0.262 0.867 Beta Demonstration study [2] Proportion culture-positive individuals with suspected TB who had antibiotic trial 1 0.051 0.241 Beta Demonstration study [2] Specificity for diagnosing pulmonary TB (SEM) Xpert MTB RIF 0.990 (0.002) 0.990 (0.002) 0.990 (0.002) Beta Demonstration study, all sites [2] Smear microscopy (two slides) 1 (—) 1 (—) 1 (—) Model assumption Mycobacterial culture 1 (—) 1 (—) 1 (—) Model assumption Clinical diagnosis 0.942 (0.009) 0.953 (0.007) 0.869 (0.030) Beta Demonstration study [2] Proportion culture-negative individuals with suspected TB who had chest X-ray 0.037 0.059 0.790 Beta Demonstration study [2] Proportion culture-negative individuals with suspected TB who had antibiotic trial 1 0.009 0.887 Beta Demonstration study [2] Sensitivity for detecting rifampicin-resistance (SEM) Xpert MTB RIF 0.944 (0.015) 0.944 (0.015) 0.944 (0.015) Beta Demonstration study, all sites [2] Conventional drug susceptibility testing 1 (—) — 1 (—) — Model assumption Line-probe assay — 1 (—) — — Model assumption Specificity for detecting rifampicin-resistance (SEM) Xpert MTB RIF 0.983 (0.005) 0.983 (0.005) 0.983 (0.005) Beta Demonstration study, all sites [2] Drug susceptibility testing 1 (—) — 1 (—) — Model assumption Line-probe assay — 1 (—) — — Model assumption Cost parameters US$ 2010 (min, max) First-line category 1 treatment: total 227 (103, 352) 454(306, 602) 185 (146, 224) Triangular WHO-CHOICE [13], literature review [14]–[19] First-line category 2 treatment: total 352 (159, 546) 998 (451, 1546) 287 (130, 445) Triangular WHO-CHOICE [13], literature review [14]–[19] Cotrimoxazol preventive treatment: 1 mo 4, 50 10, 53 3, 25 Triangular WHO-CHOICE [13] Treatment of bacterial infection 3, 66 9, 70 2, 41 Triangular WHO-CHOICE [13] Chest X-ray 11 (9, 13) 16 (14, 18) 3 (2.6, 3.7) Triangular WHO-CHOICE [13], literature review [14]–[19] Second-line treatment total 2,256 (1,463, 3,050) 3,492 (2,068, 4,917) 1,759 (1,285, 2,233) Triangular WHO-CHOICE [13], literature review [14]–[19] DALY parameters: DALYs averted (min, max) HIV positive, sputum smear-negative 9.38 (8.62, 10.39) 10.71 (9.85, 11.90) 11.58 (10.63, 12.90) Triangular See Text S1 HIV negative, sputum smear-negative 13.18 (12.32, 13.96) 13.83 (12.83, 14.72) 18.65 (17.56, 19.61) Triangular See Text S1 HIV positive, sputum smear-positive 9.67 (8.62, 10.39) 11.03 (9.85, 11.90) 11.92 (10.63, 12.90) Triangular See Text S1 HIV negative, sputum smear-positive 16.43 (16.02, 16.79) 17.52 (17.05, 17.93) 22.63 (22.13, 23.07) Triangular See Text S1 The distribution column indicates which probability distribution was specified for each parameter in the Monte Carlo simulations. For triangular distributions the mode, upper and lower limit are given. All beta distributions have boundaries (0, 1). SEM, standard error of the mean. Diagnostic test performance data were sourced from a demonstration study of Xpert use in nine facilities [2]. Sensitivity and specificity parameters for all diagnostic tests and procedures were calculated taking sputum culture as the reference standard. The sensitivity and specificity of Xpert and sputum microscopy (light-emitting diode [LED]) fluorescence microscopy) was based on weighted averages across the sites. Since clinical diagnostic practice of smear negatives in the base case varied considerably between sites, site-specific data were used to estimate performance of the clinical TB diagnosis. A patient was defined as having clinically diagnosed TB if microscopy was negative, but the onset of treatment preceded the availability of the culture result. Estimates of the economic costs of each pathway were made from a health service perspective. All costs were estimated using the ingredient costing approach. This approach identifies all the inputs (and their quantities) required to perform a test or deliver treatment and then values them to arrive at a cost per test/person treated. Diagnostic costs were collected at the demonstration sites. These costs included all building, overhead, staff, equipment and consumables, quality control and maintenance, and calibration inputs. The resource use associated with each test was measured through observations of practice, a review of financial reporting, and interviews with staff in the Xpert demonstration sites. Resource use measurement took into account the allocation of fixed resources between Xpert and any other uses. Estimates of device and test prices, calibration, and training costs were obtained from suppliers. Costs for treatment were estimated using drugs costs from the Global Drug Facility and the MSH International Price Tracker, and unit costs for outpatient visits and hospitalisation sourced from WHO-CHOICE [12]. A review of previous costing studies was used to validate these estimates [13]–[18]. As our constructed estimates were higher than those found in our review, we took the mid-point between our estimate and the lowest estimate found in the literature. All local costs were converted using the average exchange rate for 2010 (imf.statex.imf.org). Where relevant, costs were annualised using a standard discount rate of 3% [19]. All costs are reported in 2010 US$. Treatment outcome probabilities were taken from published meta-analyses of clinical trials, cohort studies, and systematic reviews [20]–[28]. DALYs averted from patients being cured were estimated using the standard formula [19]. Further details can be found in Text S1. Since the Xpert scenarios are most likely to be more costly and more effective than the base case, an incremental cost effectiveness ratio (ICER) was calculated to describe the additional cost for any additional DALYs averted by Xpert over the base case. This ICER was then compared to WHO's suggested country-specific willingness to pay (WTP) threshold, defined as the cost per DALY averted of each country's per capita GDP (US$1,134 for India, US$5,786 for South Africa, and US$490 for Uganda in 2010). If the ICER is below this threshold the intervention is considered cost-effective. In the demonstration study from which our parameter estimates were sourced [2], the probability that an individual with suspected TB was a true TB case varied considerably by location; the proportion with smear-positive TB being 8.9% in India, 14.3% in South Africa, and 32.4% in Uganda. This variation probably reflects the local patterns of (self-) referral, in particular for the extremely high proportion of TB cases among the individuals with suspected TB in Uganda. Therefore to enable generalizability, we assumed a 10% proportion of smear-positive TB in individuals with suspected TB for all three countries as our point estimate with a range of 2.5% to 25% in our uncertainty and sensitivity analyses [29]. A large number of one- and two-way sensitivity analyses were conducted to assess the robustness of our model. These analyses examine the robustness of our results when one or two parameters are varied between the outer limits of their confidence intervals. We examined the sensitivity of our results to the probability that a suspect has TB or MDR-TB or has been previously treated. We examined the impact of varying treatment costs on our results. We tested for different prices of Xpert cartridge. We examined the impact of varying the proportion of individuals with suspected TB who get chest X-ray in addition to Xpert, as physicians may continue clinical diagnosis for smear-negative TB. Similarly we examined the impact of assuming that all HIV-infected individuals with suspected TB who have negative Xpert undergo the clinical diagnosis procedure, with costs based on site-specific use of chest X-rays and antibiotics, and sensitivity and specificity based on site-specific diagnostic performance of clinical diagnosis. We assessed the sensitivity of our results to the performance of the base case in three ways: (1) assuming one instead of two smears; (2) by varying the sensitivity of smear examination; and (3) by replacing the site-specific performance estimates for clinical diagnosis with estimates averaged across the three sites. Recognising that the performance of clinical diagnosis is a trade-off between sensitivity and specificity, we varied the sensitivity and specificity in opposite directions across a plausible range of values. As physicians in the demonstration study were aware that they would receive the results of sputum culture of all individuals with suspected TB, we tested for the effect of deferring treatment decisions until the availability of culture results. For each site culture was costed and assessed on the basis of current practice. We did not include a sensitivity analysis of the use of alternatives to culture such as microscopic observation drug susceptibility test (MODS) [30], as this was not practiced on site, and we found no good source of costing data. We examined the effect of reprogramming Xpert so that no resistance result is obtained. In addition, we conducted a probabilistic sensitivity analysis (Monte Carlo simulation) to explore the effect of uncertainty across our model parameters. This analysis randomly sampled each parameter in our model simultaneously from their probability distribution (Table 1; Text S1), and repeated this 10,000 times to generate confidence intervals around our estimates of incremental cost per DALY averted. The model and the analyses were constructed using TreeAge software. Percentage ranges in the text reflect ranges across countries unless stated otherwise. The demonstration study was endorsed by national TB programmes of participating countries and approved by nine governing institutional review boards (IRBs). The requirement to obtain individual informed consent was waived. The costing and cost-effectiveness assessments were outlined in the study protocol reviewed by the IRBs. Results The cost for the Xpert test (including all costs, such as the cartridge, equipment, salaries) ranges from US$22.63 in India to US$27.55 in Uganda, at an Xpert cartridge price of US$19.40 (including a 25% mark-up for transportation) and US$17,000 per four-module instrument (Tables 2 and 3) [2]. This cost falls to as low as US$14.93 with volume-driven price reductions. As FIND has negotiated a fixed price for Xpert, the difference in costs between sites is primarily determined by the intensity of use of the four-module instrument. Other factors also influence costs, but to a lesser extent; these include local wage levels and the room space used. A single sputum smear examination costs between US$1.13 and US$1.63. Unit costs for culture (Löwenstein–Jensen [LJ] or mycobacteria growth indicator tube [MGIT]) range from US$13.56 to US$18.95. Unit costs for tests that diagnose MDR-TB (where relevant for all first-line drugs) range from US$20.23 for LPA only to US$44.88 for MGIT and LPA. 10.1371/journal.pmed.1001120.t002 Table 2 Cost of diagnostic tests at the study sites (2010 US$). Diagnostic Test Type of Laboratory Costs per Test (2010 US$) India South Africa Uganda AFB Smear (one smear) Peripheral/hospital 1.13 1.58 1.63 Xpert (current pricing) US$19.4 including transport Peripheral/hospital 22.63 25.90 27.55 Xpert (volume>1.5 million/y) US$15.5 including transport Peripheral/hospital 18.73 22.00 23.61 Xpert (volume>3.0 million/y) US$11.7 including transport Peripheral/hospital 14.93 18.20 19.85 Culture (LJ) Reference 13.56 — 15.45 Culture (MGIT) Reference — 15.24 18.95 Culture + DST (LJ) Reference 22.33 — 23.98 Culture + DST (MGIT) Reference — 41.17 44.88 DST (MGIT + LPA) Reference — 33.01 38.82 DST (LPA), on sputum Reference — 20.23 21.84 10.1371/journal.pmed.1001120.t003 Table 3 Cost of Xpert (current pricing) by input type (2010 US$). Input Type Costs per Test (2010 US$) India South Africa Uganda Overhead 0.18 0.88 0.40 Building space 0.02 0.08 0.12 Equipment 2.84 3.50 7.00 Staff 0.11 1.82 0.24 Reagents and chemicals 19.40 19.40 19.40 Consumables 0.07 0.22 0.38 Total 22.63 25.90 27.55 The use of Xpert substantially increases TB case finding in all three settings; from 72%–85% to 95%–99% of the TB suspect cohort (Table 4). When Xpert is deployed “as a replacement of” instead of “in addition to” smear microscopy, the number of TB cases detected is similar—while the number of MDR-TB cases detected increases substantially. When undiagnosed TB patients are assumed not to return for diagnosis, TB case detection increases from 62%–76% in the base case to 86%–94% in the Xpert scenarios. 10.1371/journal.pmed.1001120.t004 Table 4 Cohort, cases detected, total cohort costs, and costs per case detected. Country Scenario Cohort n Individuals among the Cohort Who Have TB Total TB Cases Detected Percent of TB Cases Detected Total MDR Cases Detected Percent of MDR Cases Detected Total Diagnostic Costs (2010 US$) Diagnostic Cost per TB Case Detected, Excluding MDR (US$ 2010) Additional Diagnostic Cost per MDR Case Detected (2010 US$) Treatment Costs (2010 US$) Treatment Costs Percent of Total Cohort India Base case Tuberculosis (MDR) 72 59 82 38 52 1,077 — — 89,223 19 Tuberculosis (no MDR) 1,318 1,079 82 — — 8,412 — — 268,122 59 No tuberculosis 8,611 — — — — 46,106 — — 100,759 22 Total 10,000 1,138 82 38 — 55,595 49 165 458,103 100 In addition to smear Tuberculosis (MDR) 72 71 99 49 68 2,335 — — 115,932 25 Tuberculosis (no MDR) 1,318 1,300 99 — — 13,831 — — 325,381 70 No tuberculosis 8,611 — — — — 184,298 — — 22,414 5 Total 10,000 1,371 99 49 200,464 146 116 463,727 100 Replacement of smear Tuberculosis (MDR) 72 71 99 67 93 3,038 — — 151,603 30 Tuberculosis (no MDR) 1,318 1,298 99 — — 28,986 — — 328,669 65 No tuberculosis 8,611 — — — — 174,538 — — 22,414 4 Total 10,000 1,369 99 67 206,562 151 24 502,687 100 South Africa Base case Tuberculosis (MDR) 184 131 72 56 31 2,345 — — 230,989 22 Tuberculosis (no MDR) 1,729 1,237 72 — — 13,772 — — 659,365 63 No tuberculosis 8,087 — — — — 22,014 — — 156,213 15 Total 10,000 1,368 72 56 38,131 28 86 1,046,567 100 In addition to smear Tuberculosis (MDR) 184 175 95 112 61 7,131 — — 423,146 31 Tuberculosis (no MDR) 1,729 1,649 95 — — 30,341 — — 882,010 65 No tuberculosis 8,087 — — — — 205,858 — — 45,788 3 Total 10,000 1,824 95 112 243,331 133 57 1,350,945 100 Replacement of smear Tuberculosis (MDR) 184 175 95 165 90 9,504 — — 583,064 39 Tuberculosis (no MDR) 1,729 1,645 95 — — 46,866 — — 880,190 58 No tuberculosis 8,087 — — — — 193,053 — — 45,788 3 Total 10,000 1,820 95 165 249,423 137 30 1,509,043 100 Uganda Base case Tuberculosis (MDR) 36 30 85 14 38 499 — — 26,422 5 Tuberculosis (no MDR) 1,882 1,594 85 — — 11,282 — — 282,928 59 No tuberculosis 8,082 — — — — 51,565 — — 171,803 36 Total 10,000 1,625 85 14 63,345 39 163 481,154 100 In addition to smear Tuberculosis (MDR) 36 34 95 22 63 1,392 — — 41,123 11 Tuberculosis (no MDR) 1,882 1,794 95 — — 34,694 — — 320,685 85 No tuberculosis 8,082 — — — — 230,369 — — 14,908 4 Total 10,000 1,828 95 22 266,455 146 124 376,717 100 Replacement of smear Tuberculosis (MDR) 36 34 95 32 90 1,849 — — 56,488 14 Tuberculosis (no MDR) 1,882 1,790 95 — — 57,204 — — 322,502 82 No tuberculosis 8,082 — — — — 217,185 — — 14,908 4 Total 10,000 1,824 95 32 276,238 151 27 393,899 100 The diagnostic cost (including the costs of testing all individuals with suspected TB) per TB case detected is US$28–US$49 for the base case and increases significantly to US$133–US$146 and US$137–US$151 when Xpert is used “in addition to” and “as a replacement of” smear microscopy, respectively, depending on the setting (Table 4). The resulting change in treatment costs is more moderate, due to a reduction in the numbers of false positives in the base case from clinical diagnosis. For example, in India, the percentage of treatment costs spent on false-positive diagnoses falls from 22% to 4% when Xpert is used “as a replacement of” smear microscopy in comparison to the base case. ICERs for each Xpert scenario are presented in Table 5. The mean ICER for using Xpert “in addition to” smear microscopy compared to the base case ranges from US$41 to US$110 per DALY averted depending on the setting. The mean ICER for using Xpert “as a replacement of” smear microscopy ranges from US$52 to US$138 per DALY averted. The mean ICER for using Xpert as “a replacement of” smear microscopy compared to using Xpert “in addition to” smear microscopy ranges between US$343 and US$650. This higher ICER is due to the fact that the effectiveness gain from using Xpert as “replacement of smear microscopy” is derived from additional MDR-TB cases detected, and the cost-effectiveness of treating MDR-TB is lower than that for drug-susceptible TB. All the ICERs found are well below the WTP threshold. 10.1371/journal.pmed.1001120.t005 Table 5 Cost per DALY (US$ 2010). Country Scenario Total Cost Total DALYS Cost per DALY ICER Compared to Base Case, Mean Monte Carlo Simulation ICER, Median (2.5, 97.5) ICER Compared to in Addition to, Mean Monte Carlo Simulation ICER, Median (2.5, 97.5) India Base case 513,698 17,133 30 — — — — In addition to smear 664,191 19,887 33 55 54 (40, 70) — — Replacement of smear 709,248 20,019 35 68 68 (51, 87) 343 361 (239, 578) South Africa Base case 1,084,698 15,805 69 — — — — In addition to smear 1,594,276 20,420 78 110 109 (88, 133) — — Replacement of smear 1,758,467 20,702 85 138 136 (105, 172) 582 594 (353, 956) Uganda Base case 544,499 22,182 25 — — — — In addition to smear 643,172 24,570 26 41 34 (−3, 69) — — Replacement of smear 670,137 24,611 27 52 37 (0, 73) 650 276 (−1895, 2,406) The results of the probabilistic sensitivity analysis (Monte Carlo simulation) are also shown in Table 5. Aside from the replacement of smear microscopy in Uganda all estimates remain cost-effective. Figure 2 provides an illustration of the cost-effectiveness of Xpert deployed as “a replacement of” smear microscopy in comparison to the “in addition to” scenario for a range of WTP thresholds. This graph, known as an acceptability curve, shows that if the WTP is US$490 in Uganda, there is around a 75% probability that Xpert as a replacement of smear is cost-effective when compared to the “in addition to” scenario. 10.1371/journal.pmed.1001120.g002 Figure 2 Cost-effectiveness acceptability curves. ICER “replacement of smear” compared with “in addition to smear.” Nearly all of our one- and two-way sensitivity analyses did not increase the ICER compared to the base case of either Xpert scenario above the WTP threshold (Table 6). Figure 3 shows ICER variation when parameters for the suspect population and the performance of the base case change. Varying the true proportion of those with TB and MDR-TB in the cohort has little effect on our results, although Xpert ICERs substantially worsen when the proportion of smear-positive TB cases becomes 5% or less (translating into 7%–9% with any type of TB). Varying assumptions on the performance of the base case alters ICERs substantially. Increasing the sensitivity of smear examination reduces the cost-effectiveness of Xpert, but not below the WTP threshold. If clinical diagnosis has a higher specificity and lower sensitivity than in our study sites, Xpert ICERs worsen, but also remain below the WTP threshold. But, if clinical diagnosis has a lower specificity and higher sensitivity than in our study sites, ICERs for Xpert substantially improve. Adding chest X-ray for 50% of the individuals with suspected TB tested by Xpert has limited impact on the cost-effectiveness of Xpert. Adding clinical diagnosis for all HIV-positive individuals with suspected TB with a negative Xpert result has no or limited effect in India and South Africa, but doubles ICERs for Xpert in Uganda (although not above the WTP threshold). This reflects differences in HIV prevalence as well as relatively high cost and low specificity of clinical diagnosis in Uganda owing to more extensive use of X-ray. Incorporating the cost of culture and increasing the proportion of TB diagnosis based on the culture result, has a mixed effect. Xpert remains cost-effective up until the point where 40%–70% of patients receive a culture-based diagnosis. Above proportions of 50%–90%, the base case becomes more effective. If however, culture performance is less than 100%, the base case does not become more effective than the Xpert-based scenarios until nearly 100% of patients receive a culture-based diagnosis (unpublished data). 10.1371/journal.pmed.1001120.g003 Figure 3 Selected sensitivity analyses. Sensitivity of the model for the prevalence of tuberculosis, for the prevalence of multidrug-resistant tuberculosis, and for the accuracy of clinical diagnosis. Patterns of ICERs in 2010 US$ for varying the proportion of individuals with suspected TB in the cohort who have smear-positive TB (A, D, G); for varying the proportion of new patients with TB who have multidrug-resistant TB (MDR-TB, B, E, H); and for varying the specificity of the clinical diagnosis of TB in the base case (C, F, I). (A, B, and C), South Africa; (E,D, and F), India; (G, H, and I), Uganda. Black lines, Xpert assay in addition to sputum smear examination; grey lines, Xpert assay as replacement of sputum smear examination. The proportion of individuals with suspected TB in the cohort who have smear-negative TB varies along with the proportion of individuals with suspected TB in the cohort who have smear-positive TB in a linear manner, depending on the HIV-infection prevalence (A, D, G; see Table 1 and Text S1). Similarly, the proportion of previously treated patients with TB who have MDR-TB varies linearly with the proportion of new patients with TB who have MDR-TB (B, E, H; see Table 1 and Text S1). The sensitivity of clinical diagnosis in the base case varies inversely with the specificity (range, 8%–80%; C, F, I). 10.1371/journal.pmed.1001120.t006 Table 6 Costs per DALY 2010 US$: sensitivity analyses. Assumption ICER Compared to: India South Africa Uganda Base Case In Addition to Smear Replacement of Smear Base Case In Addition to Smear Replacement of Smear Base Case In Addition to Smear Replacement of Smear Primary estimate Base case — 55 68 — 110 138 — 41 52 In addition to smear — 343 — — 582 — — 650 Reprogrammed test: no signal MDR Base case — 50 51 — 87 86 — 37 40 In addition to smear — 107 — — NA — — 289 Clinical diagnosis performance pooled across countries Base case — 62 78 — 89 121 — 53 58 In addition to smear — 342 — — 582 — — 650 Proportion retreatment doubles Base case — 115 119 — 209 220 — 67 73 In addition to smear — 170 — — 334 — — 200 Cartridge cost reduces to US$11.70 Base case — 42 54 — 102 129 — 26 36 In addition to smear — — 318 — 570 — — 561 50% of individuals with suspected TB have X-ray added to Xpert Base case — 73 87 — 126 154 — 47 58 In addition to smear — — 378 — — 606 — — 686 HIV-infected individuals with suspected TB have clinical diagnosis when Xpert is negative Base case — 55 68 — 132 157 — 82 90 In addition to smear — 343 — 610 — 706 Undiagnosed patients with TB do not return for diagnosis Base case — 50 67 — 109 138 — 33 43 In addition to smear — — Dominated by in addition to scenario — — 1,442 — — Dominated by in addition to scenario Single smear examination in base case Base case — 48 58 — 105 130 — 40 48 In addition to smear — — 343 — — 582 — — 650 60% of case receive culture diagnosis Base case — Dominates base case Dominates base case — 67 311 — Base case more cost-effective Base case more cost-effective In addition to smear — — 343 — — 582 — — 650 Sensitivity of smear examination increase by 15% Base case — 106 130 — 131 165 — 59 74 In addition to smear — — 343 — — 582 — — 650 NA, not available. Discussion Our results suggest that Xpert is likely to be more cost-effective than a base case of smear microscopy and clinical diagnosis of smear-negative TB. The extent and type of cost-effectiveness gain from deploying Xpert is dependent on a number of different setting-specific factors. First and foremost of these factors is the performance of current TB diagnostic practice. Where the sensitivity of current practice is low, but specificity high, Xpert has a substantial impact on effectiveness. Where the sensitivity of current practice is high, but specificity low, Xpert will lower treatment costs by reducing the number of false positives. This latter effect is illustrated by the case of Uganda, where the model predicts a reduction in the treatment costs of false positives from US$171,803 to US$14,908, contributing to the overall reduction in treatment costs. Other factors that are likely to determine the extent of cost-effectiveness gain include the proportion of those co-infected with HIV and the proportion of those with MDR-TB, and the numbers of true TB cases in the suspect population. However, our results show that increasing proportions of HIV in the suspect population will not always reduce the ICER of Xpert (Figure 3). This finding is counter-intuitive. One would expect the cost-effectiveness of a diagnostic test that diagnoses smear-negative TB to improve with increases in HIV prevalence. However, as the proportion of individuals co-infected with HIV in the suspect population increases, so the sensitivity of Xpert decreases. Depending on the relative costs and performance of the base case, this counter-effect means that the relationship between HIV prevalence and Xpert's cost-effectiveness is weaker than anticipated. Nor can we conclude on the direction of the relationship between cost-effectiveness gain and the level of prevalence of MDR-TB in the suspect population at this time. Our model demonstrates that when transmission effects are excluded, the ICER of Xpert increases as the MDR-TB prevalence increases (Figure 3). This result occurs because although the effectiveness of Xpert increases with a higher MDR-TB prevalence, the ICER of treating MDR-TB is higher than that of drug susceptible TB, thus countering the gain from increased effectiveness. Unsurprisingly, we also find that higher proportions of TB cases in the suspect population improve the cost-effectiveness of Xpert. The cost per TB case detected will also decrease with increases in TB prevalence. As TB programmes already fund elements of the base case, cost-effectiveness may therefore be initially improved by using existing diagnostic tools, such as X-ray and clinical scores, to screen the TB suspect population prior to Xpert. In the longer run, however, the expansion of X-ray as a permanent approach for suspect screening is unlikely to be cost-effective, and further work examining alternative screening approaches may be required. Moreover, different approaches are likely to be adopted for specific suspect populations, most notably those already known to be HIV infected, those who have already failed treatment, and those at a high risk of MDR-TB. We therefore recommend that further work is conducted to explore the impact on cost-effectiveness of different algorithms when Xpert is applied to more limited suspect groups. A number of factors limit our analysis. Firstly, the assumption of no transmission effects or additional mortality benefit from early diagnosis is a conservative approach and will underestimate the cost-effectiveness of Xpert—particularly where the introduction of Xpert is likely to increase the numbers of drug-resistant patients who are appropriately and rapidly treated. Likewise, we do not factor in patient costs. A full societal evaluation would make all options less cost-effective, but Xpert is likely to fare better than alternatives, as it requires fewer patient visits. In addition, if Xpert can achieve earlier diagnosis, substantial reductions in patient costs prior to treatment may be achieved [31]. The reference standard for the test performance parameters in our model did not include culture-negative TB based on response to treatment, because this diagnostic category will include cases with no TB or extra-pulmonary TB that cannot be diagnosed by sputum-based tests. This situation may have lead to overestimation of the sensitivity and underestimation of the specificity of Xpert. Owing to lack of evidence, we only included one repeat visit for false negatives in our model, to capture those who quickly progress to smear-positive TB. This number may be insufficient and miss both the additional costs and effectiveness of further repeated visits. On the other hand, our assumption that 100% of false negatives still alive and with TB after 3 mo have a repeat visit may be an overestimation, thereby inflating ICERs for the Xpert scenarios. We assumed that a negative Xpert result does not lead to further TB diagnostic procedures. This assumption may not be true in practice, in particular not for HIV-infected individuals with suspected TB [32]. Our sensitivity analyses show that adding clinical diagnostic procedures for this group can substantially reduce cost-effectiveness of Xpert when HIV prevalence is high and X-ray is used extensively. Also because of the lack of data, we have not included a high MDR-TB, but low HIV-prevalence setting. This lack of data restricts our ability to generalise findings to all low- and middle-income settings, particularly the former Soviet states, where this epidemiological pattern is common in suspect populations. Finally, our sensitivity analysis demonstrates that Xpert may not be cost-effective when compared to a base case in which a high proportion of smear-negative TB cases are diagnosed by culture. However, this result is based on our assumption that culture performs at 100% sensitivity and specificity. In addition, we did not include costs of specimen transport, increased risk of false-negative cultures or contamination, reduced sensitivity when only one specimen is cultured, and possible delay effects on mortality and patient drop out. All these simplifications will inflate the cost-effectiveness of a base case that includes culture. As is standard practice, we determine cost-effectiveness in comparison to the WHO WTP threshold. Unfortunately, achieving this threshold does not mean that the resources are available in low- and middle-income countries, merely that Xpert should be afforded [33]. In reality, resourcing for tuberculosis services in low- and middle-income countries is extremely constrained. Countries may therefore need to prioritise. In terms of priorities, suspect populations with a high likelihood of TB, particularly in settings with high HIV and MDR-TB prevalence, are an obvious choice. However, our findings illustrate that it is also important to balance these factors with the current performance of the existing diagnostic pathway. Countries or areas that have the weakest performance in terms of diagnosing smear-negative cases may benefit the most, even when they have relatively low levels of MDR-TB and HIV; although additional investment may be required to strengthen aspects of the health system to ensure that Xpert can be implemented successfully. Funding Xpert may also mean that scarce resources are not made available to other equally deserving areas. Care must therefore be exercised to take into account the broader tuberculosis control and health system needs of any particular setting when funding Xpert. Our model is robust given the current evidence and data available. However, key data in this area—particularly on the characteristics of TB suspect populations, the feasibility of implementing Xpert at scale, and the extent to which clinicians allow diagnostic test results to influence treatment decisions—remain scarce. Moreover, it is likely that there will be costs associated with Xpert scale-up that we cannot predict at this point. Although our model strongly suggests that Xpert will be cost-effective in a wide variety of settings, Xpert scale-up will substantially increase TB diagnostic costs. Given this increase, and the current data paucity, we recommend careful monitoring and evaluation of initial roll-out before full scale-up. Funding should be provided for implementation studies, including pragmatic trials, in a number of countries to accelerate this process. As we did not assess cost-effectiveness in a setting with high MDR but low HIV prevalence, we also recommend additional economic modelling studies before embarking on roll-out in these settings, taking into consideration operational factors that may affect outcomes such as patient drop-out and physician behavior [34]. Finally, although Xpert is a highly promising technology, there is still room for improvement in TB diagnostics. Xpert has incomplete sensitivity for smear-negative TB and rifampicin resistance and does not detect resistance to isoniazid and other drugs. Other promising tests, such as microscopic observation drug susceptibility test (MODS) [35], should be evaluated for their cost-effectiveness, including comparisons with Xpert. Our finding should not discourage investment in other promising new TB diagnostic technologies, particularly those that further improve the diagnostic sensitivity and detection of wider forms of drug resistance and can be implemented at peripheral health care level at low cost. Conclusion Despite the fact that there is considerable concern from policy makers about the costs and affordability of new diagnostic technologies in low- and middle-income countries, our results suggest that Xpert is likely to be a highly cost-effective investment. If demonstrated test performance is maintained at scale, Xpert has the potential to substantially increase TB case detection. Moreover, in the settings modelled, TB treatment costs are not predicted to substantially increase with the introduction of Xpert; instead, treatment is likely to be switched from those who do not benefit from treatment, to those who do. Our results suggest that funding should be provided to initiate the roll-out of Xpert in low- and middle-income countries, as a promising means of enabling access to effective treatment for all those with the disease. We recommend, however, that this roll-out is carefully evaluated to validate our results before full scale-up—to ensure that Xpert implementation is done in a way that does not negatively impact TB programmes, their funding, and the health systems that support them. Supporting Information Text S1 Details of model assumptions, test turnaround times (Table S[A]), treatment outcome probabilities (Table S[B]), probabilities of death and spontaneous recovery with false-negative tuberculosis diagnosis (Table S[C]), and variables used in the DALY calculations (Table S[D]). (DOC) Click here for additional data file.
Author and article information
Journal
Journal ID (nlm-ta): Int J Tuberc Lung Dis
Journal ID (iso-abbrev): Int. J. Tuberc. Lung Dis
Journal ID (publisher-id): jtld
Journal ID (pmc): International Union Against Tuberculosis and Lung Disease
Title:
The International Journal of Tuberculosis and Lung Disease
Publisher:
International Union Against Tuberculosis and Lung Disease
ISSN
(Print):
1027-3719
ISSN
(Electronic):
1815-7920
Publication date
(Print):
September
2014
Volume: 18
Issue: 9
Pages: 1012-1018
Affiliations
[*
]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore,
Maryland, USA
[†
]Department of Infectious Disease Epidemiology and TB Modelling Group, London School
of Hygiene & Tropical Medicine, London, UK
[§
]Department of Epidemiology and Biostatistics & McGill International TB Centre, McGill
University, Montreal, Quebec, Canada
[¶
]Department of Global Health and Amsterdam Institute for Global Health and Development,
Academic Medical Center, Amsterdam, The Netherlands
[#
]SAME Modelling and Economics, Department of Global Health and Development, London
School of Hygiene & Tropical Medicine, London, UK
Author notes
Correspondence to: David W Dowdy, Department of Epidemiology, Johns Hopkins Bloomberg
School of Public Health, 615 N Wolfe St, E6531 Baltimore, MD 21205, USA. Tel: (+1)
410 614 5022. Fax: (+1) 410 614 0902. e-mail:
ddowdy1@
123456jhmi.edu
Article
Serial Item and Contribution ID: i1027-3719-18-9-1012DOI: 10.5588/ijtld.13.0851
PMC ID: 4436823
PubMed ID: 25189546
SO-VID: 60c553d6-8780-4852-a80e-df7f8aec69b8
Copyright © © 2014 The Union
History
Date
received
: 25
November
2013
Date
accepted
: 3
April
2014
Page count
Pages: 8
Categories
Subject:
Perspectives
Data availability: